Wireless Federated Learning with Hybrid Training for Constrained IoT
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Solution Overview
Problem
Federated learning (FL) is not suitable for many hardware-constrained Internet of Things (IoT) devices with limited computing resources, and existing frameworks face challenges in managing heterogeneous computing capabilities and reducing communication overheads and privacy risks.
Innovation Solution
A wireless FL framework where users with limited computing resources send training data to a base station, while those with sufficient resources upload model parameters. The base station performs centralized learning and model aggregation, optimizing user transmit power and reception strategy through successive convex approximation (SCA) to improve data transmission rates and reduce aggregation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented on hardware-constrained IoT devices, then user privacy is protected and communication overhead is reduced, but the system cannot be deployed due to limited computing resources on these devices
Solution Approach 1:
The system segments users into two categories: FL users with sufficient computing resources who perform local model training, and CL users with limited resources who send raw data to the base station. This segmentation allows the system to accommodate heterogeneous device capabilities while maintaining the privacy benefits of federated learning for capable devices and enabling participation for resource-constrained devices through centralized processing.
Solution Approach 2:
The base station acts as an intermediary that receives raw data from CL users, performs centralized learning to generate a CL model, and then aggregates it with models from FL users. This intermediary approach enables resource-constrained devices to participate in federated learning without performing local training, effectively bridging the gap between privacy protection requirements and device capability limitations.
2Productivity
If all users upload data or models simultaneously using the same frequency band, then communication efficiency is improved and spectrum resources are saved, but signal interference increases making it difficult to separate individual signals
Solution Approach 1:
The base station employs successive interference cancellation (SIC) technology that uses feedback mechanisms to detect and subtract interfering signals iteratively. By detecting stronger signals first and canceling them out, then proceeding to detect weaker signals, the system achieves effective signal separation despite simultaneous transmissions, maintaining communication efficiency while resolving interference issues.
3Speed
If user transmit power is increased to improve data transmission rate, then communication speed increases, but aggregation error in federated learning increases due to stronger interference
Solution Approach 1:
The system jointly optimizes user transmit power and base station reception strategy as coupled parameters. By using successive convex approximation to solve the non-convex optimization problem, the system finds the optimal power allocation that achieves the required data transmission rates while minimizing the impact on aggregation accuracy through coordinated power control and interference cancellation.
4Loss of information
If centralized learning is used to collect data from edge users, then comprehensive data analysis is achieved, but communication overhead increases and transmission delays affect real-time performance
Solution Approach 1:
The system merges centralized learning and federated learning into a unified framework where the base station performs centralized learning on data from CL users and generates a CL model, which is then aggregated with locally trained models from FL users. This combination allows the system to leverage both approaches: centralized processing for resource-constrained scenarios and distributed processing for capability-rich devices, thereby reducing overall communication overhead and transmission delays compared to pure centralized learning.
Data Source
AI summary
A wireless federated learning (FL) framework and a resource optimization method are provided to resolve a problem that FL is not suitable for many hardware-constrained Internet of Things (IoT) devices with a small amount of computing resources. In the framework, users with sufficient computing resources upload locally trained model parameters to a base station, and users with limited computing resources only need to send training data to the base station. The base station performs data training and model aggregation to obtain a global model. In this way, the users with limited computing resources and the users with sufficient computing resources cooperatively train the global model. To improve a data transmission rate and reduce an aggregation error of FL, a non-convex optimization problem is constructed to jointly design user transmit power and a reception strategy of the base station, and solves the problem through a successive convex approximation (SCA) method.